error.py

import numpy as np


def cross_entropy(X,y):
    """
    X is the output from fully connected layer (num_examples x num_classes)
    y is labels (num_examples x 1)
    """
    m = y.shape[0]
    p = softmax(X)
    log_likelihood = -np.log(p[range(m),y])
    loss = np.sum(log_likelihood) / m
    return loss

def delta_cross_entropy(X,y):
    """
    X is the output from fully connected layer (num_examples x num_classes)
    y is labels (num_examples x 1)
    """
    m = y.shape[0]
    grad = softmax(X)
    grad[range(m),y] -= 1
    grad = grad/m
    return grad


class CrossEntropyError(object):
    """Multi-class cross entropy error."""

    def __call__(self, outputs, targets):
        """Calculates error function given a batch of outputs and targets.

        Args:
            outputs: Array of model outputs of shape (batch_size, output_dim).
            targets: Array of target outputs of shape (batch_size, output_dim).

        Returns:
            Scalar error function value.
        """
        return -np.mean(np.sum(targets * np.log(outputs), axis=1))

    def grad(self, outputs, targets):
        """Calculates gradient of error function with respect to outputs.

        Args:
            outputs: Array of model outputs of shape (batch_size, output_dim).
            targets: Array of target outputs of shape (batch_size, output_dim).

        Returns:
            Gradient of error function with respect to outputs.
        """
        return -(targets / outputs) / outputs.shape[0]

    def __repr__(self):
        return 'CrossEntropyError'